I wrote a guest editorial in Academic Radiology about AI readiness as a practical educational obligation, rather than an optional informatics side quest.
The piece responds to a study of medical undergraduates and radiology trainees in China, but the issue is broader: enthusiasm and exposure do not equal competence. Radiologists need enough AI literacy to recognize workflow fit, automation bias, governance gaps, and the limits of machine suggestions. Most trainees will not become model developers.
They still need to become safe, skeptical operators who can use AI with confidence and accountability in real clinical environments, worldwide, across diverse resource settings today.
Generative AI was supposed to buy us time. An eight-month field study inside a ~200-person U.S. tech company suggests it can do the opposite: it intensifies work.
First, AI lowers skill barriers, so people take on tasks they previously wouldn’t. This means designers writing code, analysts drafting research, clinicians spinning up analyses. That feels empowering, but it also creates downstream “cleanup” work for others who must review, correct, and integrate AI-assisted output.
Second, AI makes work frictionless enough that it seeps into the in-between moments. Lunch breaks, late evenings, the quick “one more prompt.” The result is blurrier boundaries and less real recovery.
Third, AI encourages parallelism: multiple drafts, multiple threads, constant checking. That boosts throughput, but it also fragments attention.
The article goes on to describe a vicious cycle in which the more your colleagues do it, the more it becomes a culture, one in which you feel compelled to keep up. Using more AI.
I’ve felt a version of this personally. A few years ago, I stopped blogging regularly to make more time for kids and life outside work. With generative AI, getting a post out is genuinely easier. It’s a idea and some prompts, edits, and reviews away. But it still has to happen sometime… which, for me, often means well into the evening, in the dark, with the quiet (adorable) snoring noises of kids nearby.
Recent research underscores a leap in neuroimaging accuracy for Alzheimer’s disease diagnosis, emphasizing the superior performance of AI-assisted radiologists over either AI or humans alone. This collaborative approach marries the meticulous precision of AI with the nuanced understanding of human experts, potentially setting a new standard in the detection of amyloid-related imaging abnormalities. Specifically, it demonstrated superior performance in detecting amyloid-related imaging abnormalities (ARIA), crucial for amyloid-β–directed antibody therapy. This synergy enhances diagnostic precision and underscores the potential of AI-enhanced radiological diagnostics to improve patient care significantly.
How will this synergy between AI and human intelligence redefine the future of medical diagnostics? Can this model be the blueprint for addressing other complex diseases? This breakthrough prompts us to envision a healthcare landscape where technology and human expertise converge to offer unparalleled patient care.
Research: Machine learning algorithm predicts wait time for outpatient imaging.
Commercial brain imaging AI receives FDA clearance
Paul Chang shares insight on the future of AI
Dreyer and Allen publish their views on the radiology AI ecosystem
CB Insights publishes market research on Google’s increasing involvement in healthcare AI.
Stay up to speed in 2 minutes. Radiology AI Briefing is a semi-regular series of blog posts featuring hand-picked news stories and summaries on machine learning and data science.
In a recent Harvard Business Review article, Ajay Agrawal and coauthors shared a simple tool to think about how an AI tool may be deployed. Although the tool is no more complex than 6 boxes of free text, it does follow a number of best practices when thinking about general data and machine learning:
Always define an end-goal – what’s the desired outcome?
You should always make a hypothesis of what may drive this desired outcome.
You should determine how to present the ML prediction in a way that drives action, not just the data itself.
Your data acquisition strategy should include a feedback mechanism.
For example, this is how one might fill out the AI Canvas tool in a radiology use case:
Prediction: Predict whether a brian MRI for a cancer patient contains increasing or new hydrocephalus
Action: Label the examination as critical, and denote that AI has determined a critical finding. For example, create an “AI-STAT” category on worklist priority.
Judgment: Compare the cost of interpreting this brain MRI at its usual turnaround time, versus immediately.
Outcome: Observe whether the action taken in response to a study labeled AI-STAT is correct.
Input: New MRIs of the brain MRI performed, and their prior studies.
Training: Historical brain MRIs
Feedback: Identify false positives – perhaps the prior study was from 20 years ago, or there’s been surgical resection, so that ex vacuo dilation of ventricles is not hydrocephalus. Perhaps there has been recent surgery Identify false negatives – subtle enlargement of the temporal horns missed by AI. Use this information to improve the AI.
How might you use this worksheet to brainstorm AI for your radiology practice?
